Although there are several effective treatments for a major depressive episode, there are no[unreadable] reliable predictors of the likelihood of remission, response or non-response with an initial trial of[unreadable] either an antidepressant medication or psychotherapy. In prioritizing a role for direct measures of[unreadable] brain functioning in the development of new algorithms for clinical management of depressed[unreadable] patients, a systematic characterization of pretreatment patterns predictive of unambiguous[unreadable] remission to standard treatments is a necessary first step. This project will characterize imagingbased[unreadable] brain subtypes that distinguish groups of never-treated depressed patients who[unreadable] subsequently respond to pharmacotherapy or cognitive behavior therapy (CBT), respectively. A[unreadable] prospectively-treated cohort of 400 never-treated depressed patients randomized to receive either[unreadable] escitalopram, duloxetine or CBT for 1.2 weeks will define these subtypes. Resting-state BOLD[unreadable] functional magnetic resonance imaging (fMRI) scans will be acquired prior to initiating[unreadable] antidepressant therapy and at a fixed, early time point specific for each treatment. Pre-treatment[unreadable] scan patterns derived using multivariate analyses and associated with the six possible response[unreadable] outcomes (3 types of response; 3 types of nonresponse) will be used to determine whether[unreadable] pretreatment brain patterns can distinguish among outcome groups. A second fMRI scan,[unreadable] acquired early in the treatment course, will be used to assess the likelihood of response to the[unreadable] specific treatment assigned. The proposed studies are a first step towards defining brain-based[unreadable] subtypes predictive of differential treatment outcome in major depression. The data from these[unreadable] studies will also be entered into more complex algorithms integrating imaging findings with[unreadable] behavioral, environmental, biochemical and genetic information for individual patients.